Me (Danielle)
UU and CBS
Last year of my PhD
The pitch
Mobility surveys
What if we used sensors?
A mobile phone app!
Lessons
Participants participate
More (short) trips with the app
Short gaps? Interpolate!
Everybody hates long gaps
12/6/2021
The project
Travel behavior as we’d like it
- Time series of coordinates
- Very precise
- Very accurate
- Very useful
Travel as we often get it
- Incomplete time series of coordinates
- Very precise
- Unknown accuracy
- Usefulness?
Guessing smart on missing data
- Take a hint from other disciplines
- Multiple imputation
How to understand multiple imputation
The plan
Imputing someone’s exact location
The plan
Imputing someone’s exact location
The plan
Imputing aggregate characteristics
Distance
Mode of transportation
Radius of gyration
The plan
Imputing aggregate characteristics
Distance
Mode of transportation
Radius of gyration
The plan
Imputing aggregate characteristics
Distance
Mode of transportation
Radius of gyration
The plan pt 2
Imputing aggregate characteristics
Incorporating time as an element
The time series data
| 8h | 9h | 10h | 11h | 12h | 13h | 14h | 15h | 16h | 17h | 18h | 19h | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id1 | 0.0 | 0.3 | 0.6 | 0.1 | 0.9 | 0.5 | 0.0 | 0.0 | 3.9 | 3.0 | 1.0 | 0.0 |
| id2 | 0.0 | 0.1 | 0.5 | 0.0 | 1.2 | 1.0 | 0.5 | 0.0 | 4.1 | 2.3 | 1.9 | 0.7 |
| id3 | 0.0 | 0.0 | 0.6 | 3.7 | 0.0 | 2.9 | 0.0 | 2.5 | 0.3 | 0.0 | 0.0 | 0.0 |
| id4 | 7.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.1 | 0.0 | 1.3 | 1.1 | 4.5 |
| id5 | 1.6 | 0.1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 7.9 | 0.0 |
| id6 | 1.5 | 4.2 | 0.0 | 0.4 | 0.0 | 0.0 | 0.1 | 0.0 | 2.1 | 0.0 | 7.1 | 4.8 |
To cumulative or not to cumulative
Distance of first 5 24-hour days.
Cum. distance in meters of first 5 24-hour days.
Deepdive
Cum. distance in meters of first 5 24-hour days.
We’ve fixed the missing data problem
Cum. distance in meters of first 2 24-hour days.
Oh, right
Cum. distance in meters of two days from one person.
But maybe there’s something to this
Cum. distance in meters of two days from two different people.
Dynamic time warping
A method of aligning time series
Can handle shifts in time
Provides us with a distance metric
We can select the best candidates for imputation!
Use in multiple imputation
Different distance weights provide different candidates
Another one
Different distance weights provide different candidates
Next steps
Middle missingness
Radius of Gyration
Allowing time shifts
Evaluation criteria
Next steps
Middle missingness
Radius of Gyration
Allowing time shifts
Evaluation criteria
Next steps
Middle missingness
Radius of Gyration
Allowing time shifts
Evaluation criteria
Next steps
Middle missingness
Radius of Gyration
Allowing time shifts
Evaluation criteria
In conclusion
- Long gaps require more complex solutions
- We can turn trajectory metrics into time series
- These time series have similarities within and between people
- Dynamic time warping allows us to align on time series similarities
- We can use multiple similar candidates in order to provide estimates for the metrics occurring during the missing time periods
Image attributions
Car vector created by vectorpocket - www.freepik.com School vector created by rawpixel.com - www.freepik.com